1
|
Huang B, Xia B, Qian J, Zhou X, Zhou X, Liu S, Chang A, Yan Z, Tang Z, Xu N, Tao H, He X, Yu W, Zhang R, Huang R, Ni D, Yang X. Artificial Intelligence-Assisted Ultrasound Diagnosis on Infant Developmental Dysplasia of the Hip Under Constrained Computational Resources. J Ultrasound Med 2023; 42:1235-1248. [PMID: 36445006 DOI: 10.1002/jum.16133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/31/2022] [Accepted: 11/06/2022] [Indexed: 05/18/2023]
Abstract
OBJECTIVES Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH. METHODS An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five-fold cross-validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost. RESULTS DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra-class correlation coefficient (ICC) on α angle of 0.96 (95% CI: 0.93-0.97), β angle of 0.97 (95% CI: 0.95-0.98), FHC of 0.98 (95% CI: 0.96-0.99) and PFD of 0.94 (95% CI: 0.90-0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds. CONCLUSIONS The proposed DDHnet demonstrate state-of-the-art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.
Collapse
Affiliation(s)
- Bingxuan Huang
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Bei Xia
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Jikuan Qian
- R&D Department, Shenzhen RayShape Medical Technology Co. Ltd., Shenzhen, China
| | - Xinrui Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xu Zhou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Shengfeng Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Ao Chang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zhongnuo Yan
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Zijian Tang
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Na Xu
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Hongwei Tao
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Xuezhi He
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Wei Yu
- Ultrasonography Department, Affiliated Shenzhen Children's Hospital, College of Medicine, Shantou University, Shenzhen, China
| | - Renfu Zhang
- Ultrasound Department, EDAN Instruments, Inc., Shenzhen, China
| | - Ruobing Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| |
Collapse
|
2
|
Cheng J, Mao Y, Hong W, Hu W, Shu P, Huang K, Yu J, Jiang M, Li L, Wang W, Ni D, Li S. Multimodal data analysis reveals that pancreatobiliary-type ampullary adenocarcinoma resembles pancreatic adenocarcinoma and differs from cholangiocarcinoma. J Transl Med 2022; 20:272. [PMID: 35705951 PMCID: PMC9199183 DOI: 10.1186/s12967-022-03473-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/05/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Ampullary adenocarcinoma (AAC) arises from the ampulla of Vater where the pancreatic duct and bile duct join and empty into the duodenum. It can be classified into intestinal and pancreatobiliary types based on histopathology or immunohistochemistry. However, there are no biomarkers for further classification of pancreatobiliary-type AAC which has important implications for its treatment. We aimed to identify the tumor origin of pancreatobiliary-type AAC by systematically analyzing whole-slide images (WSIs), survival data, and genome sequencing data collected from multiple centers. METHODS This study involved three experiments. First, we extracted quantitative and highly interpretable features from the tumor region in WSIs and constructed a histologic classifier to differentiate between pancreatic adenocarcinoma (PAC) and cholangiocarcinoma. The histologic classifier was then applied to patients with pancreatobiliary-type AAC to infer the tumor origin. Secondly, we compared the overall survival of patients with pancreatobiliary-type AAC stratified by the adjuvant chemotherapy regimens designed for PAC or cholangiocarcinoma. Finally, we compared the mutation landscape of pancreatobiliary-type AAC with those of PAC and cholangiocarcinoma. RESULTS The histologic classifier accurately classified PAC and cholangiocarcinoma in both the internal and external validation sets (AUC > 0.99). All pancreatobiliary-type AACs (n = 45) were classified as PAC. The patients with pancreatobiliary-type AAC receiving regimens designed for PAC showed more favorable overall survival than those receiving regimens designed for cholangiocarcinoma in a multivariable Cox regression (hazard ratio = 7.24, 95% confidence interval: 1.28-40.78, P = 0.025). The results of mutation analysis showed that the mutation landscape of AAC was very similar to that of PAC but distinct from that of cholangiocarcinoma. CONCLUSIONS This multi-center study provides compelling evidence that pancreatobiliary-type AAC resembles PAC instead of cholangiocarcinoma in different aspects, which can guide the treatment selection and clinical trials planning for pancreatobiliary-type AAC.
Collapse
Affiliation(s)
- Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Yize Mao
- Department of Pancreatobiliary Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Wenhui Hong
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Wanming Hu
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Peng Shu
- Molecular Laboratory, Beilun District People's Hospital, Ningbo, China
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
- Regenstrief Institute, Indianapolis, IN, USA
| | - Jingjing Yu
- Department of Pathology, Ningbo Yinzhou No.2 Hospital, Ningbo, China
| | - Maofen Jiang
- Department of Pathology, Beilun District People's Hospital, Ningbo, China
| | - Liqin Li
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou Hospital Affiliated With Zhejiang University, Huzhou, China.
| | - Wei Wang
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.
| | - Shengping Li
- Department of Pancreatobiliary Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China.
| |
Collapse
|